In typical methods for face detection and/or recognition, it is known that component-based face detection can yield better performance than global approaches, particularly when pose and illumination variations or occlusions are considered. While pose and illumination can significantly change the global face appearance, components are less prone to these changes since the components are smaller than the whole face. The component detectors may accurately locate the face components as well.
The component information may be used to register and normalize the face to a “standard” one, which is appropriate for face recognition. Also, component based methods can be used to build a detector that may handle partial occlusions. Component-based methods have also been used in other areas, such as people detection, for example.
In one prior example, a component-based face detector with a two-level hierarchy of Support Vector Machine (“SVM”) classifiers is used. The face components are detected independently with the trained SVMs at the first level. At the second level, a single SVM checks if the geometric locations of the components comply with a face. However, only the largest responses from the component detectors are used when checking the validity of the geometry. Unfortunately, SVMs are relatively slow and it would be quite challenging to employ them in real-time systems.
Another prior example employs four types of rectangular features, and uses AdaBoosting to automatically build a strong classifier from feature-based weak classifiers. This example then computes the integral image to accelerate the computation of features. This gives a high detection rate and a low false detection rate, while the boosted face detector may work in real-time.
Unfortunately, prior fusion methods typically neglect the uncertainties that characterize the component locations, and are generally unsuitable for use in the presence of noise. Accordingly, what is needed is an approach to Component Fusion for Face Detection that is suitable for use in the presence of heteroscedastic noise.